# Copyright (c) Qualcomm Innovation Center, Inc.
# All rights reserved
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import warnings
from typing import cast, Dict, List

import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper

import numpy as np
import torch
from executorch.backends.qualcomm.utils.constants import QCOM_AXIS_ORDER, QCOM_DATA

from .node_visitor import NodeVisitor
from .node_visitor_manager import register_node_visitor
from .qnn_constants import OpConcat, QNN_OP_PACKAGE_NAME_QTI_AISW


@register_node_visitor
class Cat(NodeVisitor):
    target = ["aten.cat.default"]

    def __init__(self, *args) -> None:
        super().__init__(*args)

    def define_node(
        self,
        node: torch.fx.Node,
        nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
    ) -> PyQnnWrapper.PyQnnOpWrapper:
        input_nodes = cast(List[torch.fx.Node], node.args[0])
        input_tensor_wrappers = []

        for input_node in input_nodes:
            source_input_node = self.get_node(input_node)
            input_tensor = self.get_tensor(source_input_node, node)
            input_tensor_wrappers.append(
                self.define_tensor(
                    source_input_node,
                    node,
                    input_tensor,
                    PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
                    nodes_to_wrappers,
                )
            )

        if len(input_nodes) != len(input_tensor_wrappers):
            warnings.warn(
                "[QNN Delegate Op Builder]: The number or input tensors is not equal to the number of input tensor wrappers.",
                stacklevel=1,
            )
            return

        output_tensor = self.get_tensor(node, node)
        output_tensor_wrapper = self.define_tensor(
            node,
            node,
            output_tensor,
            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
            nodes_to_wrappers,
        )

        # node args[1] might not exist
        axis = 0
        if len(node.args) == 2:
            axis = cast(int, node.args[1])

        if axis < 0:
            axis += node.meta["val"].dim()

        if QCOM_AXIS_ORDER in node.meta:
            axis = node.meta[QCOM_AXIS_ORDER].index(axis)

        concat_op = PyQnnWrapper.PyQnnOpWrapper(
            node.name,
            QNN_OP_PACKAGE_NAME_QTI_AISW,
            OpConcat.op_name,
        )
        concat_op.AddInputTensors(input_tensor_wrappers)
        concat_op.AddOutputTensors([output_tensor_wrapper])

        concat_op.AddScalarParam(
            OpConcat.param_axis,
            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
            {QCOM_DATA: np.uint32(axis)},
        )

        return concat_op
